Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

FGFR3 Testing in Urothelial Carcinoma: An Expert Commentary on Best Practices From Patient Identification to Result Reporting.

European urology oncology·2026
Same author

Substrate Carbon Controls Regional Divergence of Wetland Biofilm Carbon Sinks under Climate Warming.

Environmental science & technology·2026
Same author

Keratins and desmosomal proteins: Utility for molecular subtype identification in muscle-invasive bladder cancer.

Virchows Archiv : an international journal of pathology·2026
Same author

Successful use of upadacitinib in refractory bilateral rheumatoid pleural effusion.

Rheumatology (Oxford, England)·2026
Same author

TIM3 Signaling in Effector T Cells Acts as an Immunometabolic Switch in the Purine Degradation Pathway to Suppress Intestinal Inflammation.

Gastroenterology·2026
Same author

Expression of progranulin (GP88) protein appears as an independent prognostic factor for clinical progression in high-risk prostate cancer patients.

Scientific reports·2026

Related Experiment Video

Updated: Jan 9, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

624

Spatial transcriptomics expression prediction from histopathology based on cross-modal mask reconstruction and

Junzhuo Liu1, Markus Eckstein2, Zhixiang Wang3

  • 1Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany.

Medical Image Analysis
|December 2, 2025
PubMed
Summary

This study introduces a deep learning method using contrastive learning to predict spatial gene expression from whole-slide images, overcoming data limitations in spatial transcriptomics for cancer research.

Keywords:
Contrastive learningHistopathologyMultimodal fusionSpatial transcriptomics

More Related Videos

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

5.3K
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

4.2K

Related Experiment Videos

Last Updated: Jan 9, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

624
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

5.3K
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

4.2K

Area of Science:

  • Computational biology
  • Genomics
  • Biomedical imaging

Background:

  • Spatial transcriptomics provides crucial gene expression data for tumor microenvironment analysis and cancer diagnosis.
  • Acquiring large-scale spatial transcriptomics data is challenging due to high costs.
  • Existing methods struggle with limited spatial transcriptomics datasets.

Purpose of the Study:

  • To develop a novel deep learning method for predicting spatially resolved gene expression from whole-slide images (WSIs).
  • To establish a correspondence between histopathological morphology and spatial gene expression using multi-modal contrastive learning.
  • To enhance feature-level fusion between modalities via cross-modal masked reconstruction.

Main Methods:

  • A contrastive learning-based deep learning framework is proposed.
  • Multi-modal contrastive learning aligns histopathological features with gene expression.
  • Cross-modal masked reconstruction is employed as a pretext task for feature fusion.
  • The method does not require large pretraining datasets or abstract semantic representations.

Main Results:

  • The method accurately predicts spatially resolved gene expression from WSIs.
  • Significant improvements in Pearson Correlation Coefficient (PCC) were observed for predicting highly expressed, highly variable, and marker genes (6.27%, 6.11%, and 11.26% increase, respectively).
  • The approach preserves gene-gene correlations and is effective with limited sample datasets.
  • Potential for cancer tissue localization based on biomarker expression was demonstrated.

Conclusions:

  • The developed method offers an effective solution for predicting spatial gene expression from WSIs, particularly in data-limited scenarios.
  • This approach enhances the utility of spatial transcriptomics in cancer research and clinical diagnosis.
  • The method shows promise for advancing computational pathology and biomarker discovery.